Conversely, NAR architectures are a poor inductive bias as they require significantly more computation to perform competitively to AR decoders.
For instance, recent work [1]}, [2]} used more than 30 layers with over 10 Million parameters.
We believe that such overparameterized networks are able to memorize all patterns for small TSP training sizes [3]},
but the learnt policy is unable to generalize beyond training graph sizes.
At the same time, when compared fairly within the same codebase, NAR decoders are significantly faster than AR decoders described in Section REF  as well as those which re-embed the graph at each decoding step [4]}, see Figure REF .
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